Paper Info
Title
What Computers Can Teach Us About Doctor-Patient Communication: Leveraging Gender Differences in Cancer Care
Abstract
Advanced cancer patients sometimes spend their final days in unnecessary distress while receiving aggressive cancer treatment that is unlikely to work. Part of this problem stems from patients having incorrect understanding of their prognosis. Although studies have identified that effective doctor-patient communication is associated with better patient outcomes, most cancer patients misunderstand their prognosis. We applied computational language analysis tools (word category and language sentiment) to identify gender-specific communication characteristics associated with improved patient prognosis understanding. Analysis of 382 conversations between oncologists and patients identified that for female doctors, discussing feelings, using positive sentiment language, and speaking in shorter turns were strongly associated with better patient prognosis understanding. For male doctors, allowing patients to speak more, discussing the future, and not focusing heavily on religion or death were important. Synchrony between the doctors and patients usage of positive sentiment language was shown to be relevant only for female doctors.
Year
DOI
Venue
2019
10.1109/ACII.2019.8925471
2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
Keywords
Field
DocType
Cancer care,Patient-centered communication,Sentiment
Social psychology,Distress,Prognostics,Language analysis,Family medicine,Computer science,Aggressive cancer,Patient communication,Cancer,Feeling
Conference
ISSN
ISBN
Citations 
2156-8103
978-1-7281-3889-3
0
PageRank 
References 
Authors
0.34
5
7
Name
Order
Citations
PageRank
Mohammad Rafayet Ali1146.93
Taylan K. Sen253.82
Viet-Duy Nguyen332.14
Mohammed (Ehsan) Hoque418720.88
Ronald M. Epstein500.34
Reza Rawassizadeh624322.24
Paul Duberstein710.75